#### Get the ultimate guide to Data Contracts Deep Dive [Get Guide](/content/data-contracts-specification-deep-dive/index.html) #### Get the ultimate guide to Data Contracts as Code [Get Guide](/content/the-leaders-guide-to-implementing-data-contracts-as-code/index.html) #### Discover where your data _really_ comes from. [Download Now](/content/where-did-my-data-come-from/index.html) #### Ultimate Guide to Data Contracts [Download eBook](/content/data-contracts-book/index.html) Data governance is becoming a commonly critical aspect of modern enterprise operations. In fact, [64% of data leaders](https://humansofdata.atlan.com/2024/03/future-of-data-analytics-2024/) surveyed say governance is a priority in 2024. This makes sense. GPTs, LLMs, machine learning, and the adoption of cloud services and cloud computing are becoming table stakes for the average organization (if they aren’t already). It technically follows that organizations should do more than simply adopt and refine [data governance practices](/content/blog/federated-vs-centralized-data-governance/index.html); they should look to implement data governance as code (DGaaC). Because business stakeholders are increasingly relying on real-time, [high-quality data](/content/blog/data-quality/index.html) in their daily operations (not just in key strategic moments), the stakes related to data management are getting intense. This means organizational data lifecycles need more than just run-of-the-mill, human policing. With organizational success on the line, entrusting enforcement to machines is becoming the best way forward. When it comes to data governance, every organization needs a RoboCop. ## Using data quality as code to automate data quality enforcement In theory, governance as code builds on [traditional data governance](/content/blog/data-governance/index.html)—the mission-critical approach to managing and enforcing data governance policies. In practice, governance as code leverages the principles and practices of software development and engineering, particularly the concept of infrastructure as code (IaC), and applies them to policing exceptional data quality. This provides data governance as code a sturdy initial framework, one designed to enhance what human professionals can do within an organization as opposed to replacing them outright. ### Automation of policies Governance as code will leverage code to automate the monitoring and enforcement of data governance policies and compliance. Doing so allows for real-time policy enforcement, which reduces reliance on manual processes. ### Version control and modularity DGaaC will emphasize the use of version control systems to manage necessary changes to governance policies—meaning all modifications will be tracked and that previous versions can be restored (if necessary). ### Continuous integration and deployment By leveraging continuous integration and deployment (CI/CD) practices, governance as code enables rapid and reliable deployment of governance policies themselves. ### Collaboration and agility Data governance as code will [foster collaboration](/content/blog/data-collaboration/index.html) between different teams (e.g., operations, development, security) as it establishes a common framework for defining and enforcing policies. ### Declarative policy definition DGaaC will also employ simple declarative language to define governance policies, making it easier for various stakeholders to understand and implement these critical policies. ## 3 Principles of data governance as code ### 1. Ensure data integrity _DGaaC will work to ensure that organizational data remains accurate, reliable, and consistent throughout the organization._ ### 2. Safeguard data privacy and security _It will protect the innocent and the organization’s reputation by prioritizing the protection of sensitive and personal data._ ### 3. Enforce compliance _DGaaC will uphold the law by ensuring all data governance policies and regulatory requirements are strictly enforced through code._ Together, these principles provide a clear, directive-driven framework that ensures data professionals and stakeholders guide the implementation of data governance as code. ## The distinct advantages of embracing data governance as code The foundational aspects of DGaaC we’ve mentioned here certainly benefit organizations. But the advantages of employing a RoboCop tend to positively affect the teams they work on behalf of as well. ### Automation enablement DGaaC supports the implementation and optimization of data governance policies and procedures through automated scripts and tools. ### Increased scalability Businesses need to grow. This gets trickier for data teams handling governance manually. ### All-encompassing data consistency The understated beauty of putting compliance RoboCops to work is how consistently they deliver data quality. ### Enhanced agility Data teams who handle data governance through code can make large-scale changes to procedures and policies whenever necessary. ### Comprehensive compliance With DGaaC, this agility doesn’t come at a cost. Organizations with automated data governance processes can trust their data handling practices are consistently aligned with all regulatory standards. ## A note on sequels: Similarities and differences between data and cloud governance as code Success begets success. ### 1. Shared foundations: Automation and compliance Both CGaC and DGaaC build on the principles of Governance as Code, leveraging automation. ### 2. Context and scope: Different operating environments #### Data Governance as Code **Broad application:** DGaaC is applied across all data environments. #### Cloud Governance as Code **Cloud-specific focus:** CGaC is tailored to the unique challenges of cloud environments. ### 3. Challenges addressed: Unique vs. overlapping issues While DGaaC and CGaC share some common challenges, they also address distinct issues unique to their respective domains: #### DGaaC challenges **Data consistency across platforms:** Ensuring consistent governance policies across varied data systems. #### CGaC challenges **Cloud resource management:** Managing the governance of dynamic, often transient cloud resources. #### Complementary approaches: Building a holistic approach to data policing Together, they form a holistic governance strategy that ensures robust, end-to-end governance across an organization's entire data ecosystem.